Access the full text.
Sign up today, get DeepDyve free for 14 days.
Y. Mazaheri, A. Shukla-Dave, H. Hricak, S. Fine, Jingbo Zhang, Gloria Inurrigarro, C. Moskowitz, N. Ishill, V. Reuter, K. Touijer, K. Zakian, J. Koutcher (2008)
Prostate cancer: identification with combined diffusion-weighted MR imaging and 3D 1H MR spectroscopic imaging--correlation with pathologic findings.Radiology, 246 2
Tatsuo Gondo, H. Hricak, E. Sala, Junting Zheng, C. Moskowitz, Melanie Bernstein, J. Eastham, H. Vargas (2014)
Multiparametric 3T MRI for the prediction of pathological downgrading after radical prostatectomy in patients with biopsy-proven Gleason score 3 + 4 prostate cancerEuropean Radiology, 24
F. Tixier, C. Rest, M. Hatt, N. Albarghach, O. Pradier, J. Metges, L. Corcos, D. Visvikis (2011)
Intratumor Heterogeneity Characterized by Textural Features on Baseline 18F-FDG PET Images Predicts Response to Concomitant Radiochemotherapy in Esophageal CancerThe Journal of Nuclear Medicine, 52
Emilie Niaf, O. Rouvière, F. Mege-Lechevallier, F. Bratan, C. Lartizien (2012)
Computer-aided diagnosis of prostate cancer in the peripheral zone using multiparametric MRIPhysics in Medicine and Biology, 57
S. Salami, M. Vira, B. Turkbey, M. Fakhoury, O. Yaskiv, R. Villani, Eran Ben‐Levi, A. Rastinehad (2014)
Multiparametric magnetic resonance imaging outperforms the Prostate Cancer Prevention Trial risk calculator in predicting clinically significant prostate cancerCancer, 120
T. Yoo, M. Ackerman, W. Lorensen, W. Schroeder, V. Chalana, S. Aylward, Dimitris Metaxas, R. Whitaker (2002)
Engineering and algorithm design for an image processing Api: a technical report on ITK--the Insight Toolkit.Studies in health technology and informatics, 85
Yahui Peng, Yulei Jiang, Cheng Yang, J. Brown, T. Antic, I. Sethi, C. Schmid-Tannwald, M. Giger, S. Eggener, A. Oto (2013)
Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score--a computer-aided diagnosis development study.Radiology, 267 3
L. Klotz, Liying Zhang, Adam Lam, R. Nam, A. Mamedov, A. Loblaw (2010)
Clinical results of long-term follow-up of a large, active surveillance cohort with localized prostate cancer.Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 28 1
S. Viswanath, N. Bloch, J. Chappelow, R. Toth, N. Rofsky, E. Genega, R. Lenkinski, A. Madabhushi (2012)
Central gland and peripheral zone prostate tumors have significantly different quantitative imaging signatures on 3 tesla endorectal, in vivo T2‐weighted MR imageryJournal of Magnetic Resonance Imaging, 36
J. Barentsz, J. Richenberg, R. Clements, P. Choyke, S. Verma, G. Villeirs, O. Rouvière, V. Løgager, J. Fütterer (2012)
ESUR prostate MR guidelines 2012European Radiology, 22
R. Haralick (1979)
Statistical and structural approaches to textureProceedings of the IEEE, 67
H. Vargas, O. Akin, T. Franiel, Y. Mazaheri, Junting Zheng, C. Moskowitz, Kazuma Udo, J. Eastham, H. Hricak (2011)
Diffusion-weighted endorectal MR imaging at 3 T for prostate cancer: tumor detection and assessment of aggressiveness.Radiology, 259 3
Liang Wang, Y. Mazaheri, Jingbo Zhang, N. Ishill, K. Kuroiwa, H. Hricak (2008)
Assessment of biologic aggressiveness of prostate cancer: correlation of MR signal intensity with Gleason grade after radical prostatectomy.Radiology, 246 1
G. Aus, C. Abbou, M. Bolla, A. Heidenreich, H. Schmid, H. Poppel, J. Wolff, F. Zattoni (2001)
EAU guidelines on prostate cancer.European urology, 48 4
R. Conners, M. Trivedi, C. Harlow (1984)
Segmentation of a high-resolution urban scene using texture operatorsComput. Vis. Graph. Image Process., 25
A. Huynen, R. Giesen, J. Rosette, R. Aarnink, F. Debruyne, H. Wijkstra (1994)
Analysis of ultrasonographic prostate images for the detection of prostatic carcinoma: the automated urologic diagnostic expert system.Ultrasound in medicine & biology, 20 1
O. Donati, Y. Mazaheri, A. Afaq, H. Vargas, Junting Zheng, C. Moskowitz, H. Hricak, O. Akin (2014)
Prostate cancer aggressiveness: assessment with whole-lesion histogram analysis of the apparent diffusion coefficient.Radiology, 271 1
S. Jung, O. Donati, H. Vargas, D. Goldman, H. Hricak, O. Akin (2013)
Transition zone prostate cancer: incremental value of diffusion-weighted endorectal MR imaging in tumor detection and assessment of aggressiveness.Radiology, 269 2
A. Heidenreich, P. Bastian, J. Bellmunt, M. Bolla, S. Joniau, T. Kwast, M. Mason, V. Matveev, T. Wiegel, F. Zattoni, N. Mottet (2014)
EAU guidelines on prostate cancer. part 1: screening, diagnosis, and local treatment with curative intent-update 2013.European urology, 65 1
A. Thrift, D. Whiteman (2013)
Can we really predict risk of cancer?Cancer epidemiology, 37 4
N. Coffey, N. Schieda, G. Cron, Previn Gulavita, K. Mai, T. Flood (2015)
Multi‐parametric (mp) MRI of prostatic ductal adenocarcinomaJournal of Magnetic Resonance Imaging, 41
R. Haralick, K. Shanmugam, I. Dinstein (1973)
Textural Features for Image ClassificationIEEE Trans. Syst. Man Cybern., 3
J. Barentsz, A. Villers, M. Schouten (2013)
Reply to Letter to the Editor re: ESUR prostate MR guidelinesEuropean Radiology, 23
E. Oczeretko, M. Borowska, Agnieszka Kitlas, A. Borusiewicz, Malgorzata Sobolewska-Siemieniuk (2008)
Fractal analysis of medical images in the irregular regions of interest2008 8th IEEE International Conference on BioInformatics and BioEngineering
Kent Martin, B. Hoffman (2008)
Mastering CMake: A Cross-Platform Build System
Shan Tan, S. Kligerman, Wengen Chen, Minh Lu, Grace Kim, S. Feigenberg, W. D'Souza, M. Suntharalingam, W. Lu (2013)
Spatial-temporal [¹⁸F]FDG-PET features for predicting pathologic response of esophageal cancer to neoadjuvant chemoradiation therapy.International journal of radiation oncology, biology, physics, 85 5
T. Hambrock, D. Somford, H. Huisman, I. Oort, J. Witjes, C. Kaa, T. Scheenen, J. Barentsz (2011)
Relationship between apparent diffusion coefficients at 3.0-T MR imaging and Gleason grade in peripheral zone prostate cancer.Radiology, 259 2
N. deSouza, S. Riches, N. Vanas, V. Morgan, S. Ashley, C. Fisher, G. Payne, Chris Parker (2008)
Diffusion-weighted magnetic resonance imaging: a potential non-invasive marker of tumour aggressiveness in localized prostate cancer.Clinical radiology, 63 7
D. Lopes, G. Ramalho, F. Medeiros, Rodrigo Costa, R. Araújo (2006)
Combining Features to Improve Oil Spill Classification in SAR Images
Several Haralick-based texture features appear useful for prostate cancer detection and GS assessment. Key Points • Several Haralick texture features may differentiate non-cancerous and cancerous prostate tissue. • Tumour Energy and Entropy on ADC maps correlate with Gleason score. • T2w-image-derived texture features are not associated with the Gleason score.
European Radiology – Springer Journals
Published: May 21, 2015
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.